Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards

• Published in: Scientific reports Vol. 14; no. 1; p. 4707
• Main Authors: Jeon, Yonghyuk, Kim, You Sun, Jang, Wonjin, Park, June Dong, Lee, Bongjin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.02.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/499; Age; Cardiopulmonary resuscitation; Child; CPR; Datasets; Deep Learning; Hospitalization; Humanities and Social Sciences; Humans; Intensive Care Units; multidisciplinary; Observational studies; Patients; Patients' Rooms; Pediatrics; Prediction models; Retrospective Studies; ROC Curve; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-55528-1

Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables. This retrospective observational study included patients under the age of 18 who were admitted to the general ward of a tertiary children’s hospital between 2020 and 2022. A critical event was defined as cardiopulmonary resuscitation, unplanned transfer to the intensive care unit, or mortality. The vital signs measured during hospitalization, their measurement intervals, sex, and age were used to train a critical event prediction model. Age-specific z-scores were used to normalize the variability of the normal range by age. The entire dataset was classified into a training dataset and a test dataset at an 8:2 ratio, and model learning and testing were performed on each dataset. The predictive performance of the developed model showed excellent results, with an area under the receiver operating characteristics curve of 0.986 and an area under the precision-recall curve of 0.896. We developed a deep learning model with outstanding predictive power using simplified variables to effectively predict critical events while reducing the workload of medical staff. Nevertheless, because this was a single-center trial, no external validation was carried out, prompting further investigation.